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SPArse Modeling Software now opensource!
About
What is SPAMS?
SPAMS (SPArse Modeling Software) is an optimization toolbox for solving various sparse estimation problems.
 Dictionary learning and matrix factorization (NMF, sparse PCA, ...)
 Solving sparse decomposition problems with LARS, coordinate descent, OMP, SOMP, proximal methods
 Solving structured sparse decomposition problems (l1/l2, l1/linf, sparse group lasso, treestructured regularization, structured sparsity with overlapping groups,...).
See the documentation for all the features.
It is developped by Julien Mairal (INRIA), with the collaboration of Francis Bach (INRIA), Jean Ponce (Ecole Normale Supérieure), Guillermo Sapiro (University of Minnesota), Rodolphe Jenatton (INRIA) and Guillaume Obozinski (INRIA).
It is coded in C++ with a Matlab interface. Recently, interfaces for R and Python have been developed by JeanPaul Chieze (INRIA), and archetypal analysis was written by Yuansi Chen (UC Berkeley) during an internship at INRIA.
License
Version 2.1 and later are opensource under licence GPLv3.
It is therefore possible to redistribute the sources with any other software, as long as it under GPL licence.
For other usage, please contact the authors.
A monograph about sparse estimation
Related publications
You can find here some publications at the origin of this software. The "matrix factorization" and "sparse decomposition" modules were developed for the following papers:
The "proximal" module was developed for the following papers:
The feature selection tools for graphs were developed for"
The incremental and stochastic proximal gradient algorithm correspond to the following papers
News
25/05/2014: SPAMS v2.5 is released.
12/05/2013: SPAMS v2.4 is released.
05/23/2012: SPAMS v2.3 is released.
03/24/2012: SPAMS v2.2 is released with a Python and R interface, and new compilation scripts for a better Windows/Mac OS compatibility.
06/30/2011: SPAMS v2.1 goes opensource!
11/04/2010: SPAMS v2.0 is out for Linux and Mac OS!
02/23/2010: Windows 32 bits version available! ElasticNet is implemented.
10/26/2009: Mac OS, 64 bits version available!
